Phenological benchmarking with a land surface model: an in-silico experiment for temperate forests

Phenological benchmarking with a land surface model: an in-silico experiment for temperate forests | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Phenological benchmarking with a land surface model: an in-silico experiment for temperate forests View ORCID Profile Midori Yajima , View ORCID Profile Josua Seitz , View ORCID Profile William J. Matthaeus , View ORCID Profile Gayathri Girish Nair , View ORCID Profile Luke Daly , View ORCID Profile Silvia Caldararu doi: https://doi.org/10.1101/2025.11.20.689458 Midori Yajima 1 Discipline of Botany, School of Natural Sciences, Trinity College Dublin, College Green , Dublin 2, D02 PN40, Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Midori Yajima For correspondence: myajima{at}tcd.ie Josua Seitz 1 Discipline of Botany, School of Natural Sciences, Trinity College Dublin, College Green , Dublin 2, D02 PN40, Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Josua Seitz William J. Matthaeus 1 Discipline of Botany, School of Natural Sciences, Trinity College Dublin, College Green , Dublin 2, D02 PN40, Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for William J. Matthaeus Gayathri Girish Nair 1 Discipline of Botany, School of Natural Sciences, Trinity College Dublin, College Green , Dublin 2, D02 PN40, Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gayathri Girish Nair Luke Daly 1 Discipline of Botany, School of Natural Sciences, Trinity College Dublin, College Green , Dublin 2, D02 PN40, Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luke Daly Silvia Caldararu 1 Discipline of Botany, School of Natural Sciences, Trinity College Dublin, College Green , Dublin 2, D02 PN40, Dublin, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Silvia Caldararu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Plant phenology affects biotic interactions, water, carbon and nutrient cycling, ultimately influencing global climate, and making understanding and quantifying phenological dynamics central for ecological forecasting. This however varies depending on data source, smoothing methodology or date-extraction method. The selection of an appropriate phenological processing pipeline for the system of study thus requires accurate benchmarking in a controlled environment. In this study we use a Land Surface Model (LSM) to simulate ecosystem dynamics at four temperate forest sites under five lesser studied climate perturbations. We use the generated synthetic dataset to control for environmental conditions while testing commonly used phenological detection methods (relative thresholds at 10%, 20% and 50%, highest rate of change, detection of inflection points in the curvature’s angle, detection of recovery and senescence points). Amongst tested methods, we find general agreement in terms of response to climate alterations. Inflection and low thresholds (10%) methods provide the closest match to the model’s predicted start of season, with a maximum discrepancy of 4 days between the two. Higher thresholds (20%, 50%) are closer to the models’ predicted end of season, although with discrepancies of up to 10 days. In addition, high thresholds and methods based on the rate of change of vegetation parameters successfully detect the peak of season. We also show how some methods can pick up on effects that are ignored by temperature-only type growing seasons, thereby supporting the need for increased realism of phenological representations in land surface models. This is, to our knowledge, the first time an LSM is used to generate an ‘in silico’ experiment. This study not only tests phenological extraction methodologies within a common framework, but demonstrates how land surface models can be used as tools for ecological testing beyond their current use as predictive tools. Introduction Phenology refers to the timing of seasonal events and their biotic and abiotic drivers, a process that affects nearly every aspect of life on earth ( Richardson et al., 2013 ). In extra-tropical regions, seasonality drives different aspects of plant activity, including plant growth, net carbon gain, or occurrence of specific events (e.g. budburst, flowering, leaf greenup, coloration and fall), that commonly define a growing season ( Körner et al., 2023 ). The timing of these events shapes reproductive success, biotic interactions, and ecosystem functioning through water, nutrient and carbon cycling, ultimately affecting the ecosystems’ energy balance ( Forrest & Miller-Rushing, 2010 ; Katal et al., 2022 ; Silvestro et al., 2025 ; Chen et al., 2022 ). This results in feedback loops between terrestrial ecosystem functioning and the global climate, where the timing of phenological events has direct consequences on the climate system on one side, but also responds to climatic changes ( Keenan et al., 2014 ) with increased temperatures and shifts in precipitation patterns affecting onset and duration of a growing season ( Campioli et al., 2024 ). View this table: View inline View popup Table 1. Abbreviations used in this study and their description Assessing and modelling phenology is therefore central to understand how ecosystems both contribute and respond to environmental conditions ( Katal et al., 2022 ). Traditionally, phenological research investigates data and metrics related to temperature responses of deciduous species in temperate climates, and numerous studies have focused on the effects of warmer springs on advancing the growing season ( Hassan et al., 2023 ; Shi et al., 2025 ; Stuble et al., 2021 ), and to a lesser degree on the effects of warmer autumns on delaying leaf off ( Calinger & Curtis, 2023 ). However, globally, many other seasonal patterns exist including dry-wet season dynamics in the tropics ( Abernethy et al., 2018 ) and functional seasonality in boreal evergreen forests ( Yang et al., 2020 ). Even in temperate regions change in environmental conditions beyond temperature can affect seasonality. For example, dry but bright springs have been shown to lead to earlier greening and increase in productivity ( Bastos et al., 2020 ). In addition, our understanding of phenology is also limited by the variety of data source and detection methodologies used to derive growing season metrics. Phenological data sources span from traditional point observations of leaf-on and leaf-off timing, high resolution ground measurements of greenness and productivity, to decades long timeseries of remote sensed vegetation indexes ( Gallinat et al., 2021 ; Gray & Ewers, 2021 ; Katal et al., 2022 ; Reyes-González et al., 2021 ). To align the different data sources and our understanding of growing season dynamics, we generally derive start and end of season metrics from timeseries through a variety of mathematical de-noising techniques and phenological dates detection methods ( Caparros-Santiago et al., 2021 ; Klosterman et al., 2014 ; Panwar et al., 2023 ). This inevitably leads to uncertainty as the type of observation measures different ecosystem properties, and the mathematical model and phenological extraction method detects different points in the growing season. Further, while traditional tree phenology is concerned with the time of leaf appearance and disappearance, by either direct observation or calculation from greenness timeseries, the study of ecosystem carbon dynamics focuses more on the seasonality of productivity, frequently measured or modelled as gross primary production (GPP). While leaf growth and productivity are inherently interlinked the dependency is non-linear and the phenological impacts could differ ( Körner et al., 2023 ). This not only limits our understanding of vegetated systems response to climate change but also their ecological forecasting, as the evaluation of model performances would be based on non-representative phenological metrics. The selection of the most appropriate phenological set of data and processing pipeline for the system of study should therefore follow accurate benchmarking, to determine whether detected differences in the derived growing season are climate- or methodology-related. This is however difficult to determine from experimental and observational data, where climatic drivers and local contingencies cannot be easily disentangled, complicating the attribution of phenological patterns to either external variables or phenological detection methods ( Wolkovich et al., 2012 ). For this reason, in this study we simulate four temperate forest sites with the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system, Thum et al., 2019 ) Land Surface Model (LSM) to provide a synthetic dataset where environmental conditions can be controlled. We test four commonly used methodologies to extract the start (SOS) and end (EOS) of a growing season on two simulated vegetation processes (Gross Primary Productivity and Leaf Area Index) under five lesser studied climate scenarios. We then assess the derived growing season metrics against QUINCY’s in-built temperature dependent growing season. This aims to assess how phenological transition dates differ when carbon uptake and canopy dynamics are considered across detection methods, the sensitivity of each method to detect climate changes, as well as how they compare against a theoretical ‘true’ growing season. 2. Materials and Methods 2.1 Site Description We selected four sites dominated by deciduous broadleaf forest (DBF) from the PLUMBER2 dataset ( Ukkola et al., 2022 ), which comprises of 170 flux tower sites globally for use in land surface modelling. The selected sites, namely Hainich (DE-Hai), Fontainebleau-Barbeau (FR-Fon), Harvard Forest (US-Ha1), and Morgan Monroe State Forest (US-MMs), span a range of mean annual temperature (MAT) and precipitation (MAP), reflecting the climatic extent of temperate broadleaf forests in the dataset (Figure S1). We did not consider representativeness of species or soil type, and only meteorological data from the sites were used to drive the model. However we observe similar structural characteristics between sites, with a woody vegetation cover > 60% and dominant vegetation type representative of deciduous broadleaf forests ( Berveiller et al., 2005–2014 ; Knohl et al., 2000–2012 ; Munger, 1991–2012 ; Novick & Phillips, 1999–2014 ). An overview of site characteristics can be found in Table 2 . View this table: View inline View popup Download powerpoint Table 2. Deciduous broad leaf forest sites selected from the PLUMBER2 dataset, including Site ID, Site Name, Latitude (°), Longitude (°), Elevation (m), Mean Annual Temperature (MAT, °C) and Mean Annual Precipitation (MAP, mm) 2.2 Model overview and simulations For each site, we simulated ecosystem dynamics using QUINCY, a land surface model with fully coupled carbon (C), nitrogen (N), phosphorus (P) and water cycles. QUINCY is structured to follow the flow of these nutrients through soil and vegetation across a set of 14 plant functional types (PFTs) characterised by different leaf type, phenological and photosynthesis strategies. For each PFT, vegetation features structural pools (leaf, fruit, sapwood, heartwood, fine root, coarse root) and storage pools, further divided into labile - where carbon and nutrients are temporarily stored prior to their allocation, and reserve - long term storage. Resources obtained by either photosynthesis or nutrient uptake are thus allocated between pools, balancing between tissue production, maintenance respiration, and the storage of resources for the following season. Resulting plant functions rely on PFT-specific structural characteristics, climatic variables, soil characteristics and nutrients as well as water availability. Simulated vegetation parameters used in this study are Gross Primary Productivity (GPP, photosynthesis integrated to the canopy) and Leaf Area index (LAI). QUINCY calculates photosynthesis for sunlit and shaded leaves for each canopy layer following Kull and Kruijt (1998) and photosynthetic capacity depends on leaf N content, which in turn is driven by a balance of N demand for growth and soil available N. Photosynthesis is downregulated in the form of an empirical function by the availability of nutrients and soil moisture, and photosynthetic parameters acclimate to temperature following Friend (2010) . The LAI derives from Specific Leaf Area (SLA) characteristic to each PFT (15.4 mm/mgDW for DBF) and leaf mass. The latter depends on the allocation and partitioning of newly assimilated carbon and nutrients to available pools, given by dynamic allometry based on pipe theory and nutrient and water availability. Meristem control further downregulates growth in unfavourable temperature or moisture conditions. In terms of phenology, meteorological triggers control QUINCY’s start (SOS) and end (EOS) of the growing season, with plant growth set to zero outside of this period. The model differentiate between phenological strategies among PFTs, so that SOS for DBF is a function of the heat accumulation since last dormancy, expressed as growing degree days (𝐺𝐷𝐷 acc ) above a PFT-specific temperature threshold ( ), as: where 𝐺𝐷𝐷 acc is the current growing degree day above (5°C for DBF), as the temperature threshold, (800.0 °C days for DBF) is a PFT-specific maximum requirement for growing degree days in absence of chilling, (0.007 days -1 for DBF) is a scaling factor to account for chilling requirements of the buds, 𝑁𝐷𝐷 is the number of days of dormancy since the last growing season, t air is the current air temperature (°C), and 𝑑𝑡 is the time-step. The EOS is in turn triggered by decreasing average air temperatures below a PFT-specific temperature threshold (8.5 °C for DBF). Temperature-related parameters are expressed in degrees Celsius, 𝐺𝐷𝐷, 𝑁𝐷𝐷 and 𝑑𝑡 are expressed in days. The SOS and EOS in the model hence indicates the period when plant growth can occur, but the plant growth itself is controlled by environmental conditions as nutrient and water availability, so that LAI and GPP development can be decoupled from the strict growing season even if temperature requirements for SOS are met. In QUINCY, model simulations are driven using input data for atmospheric conditions, air humidity, soil characteristics, and atmospheric CO2 concentrations as well as deposition rates of N, and P. The atmospheric inputs including half-hourly measurements of air temperature, precipitation, incoming radiation, air pressure, humidity, and wind speed, are sourced from site-specific observations provided by the PLUMBER2 dataset ( Ukkola et al., 2022 ). Soil parameters are derived from the SoilGrids database ( Hengl, 2017 ), atmospheric CO2 concentrations are based on Le Quéré et al. (2018) , nitrogen deposition data come from Lamarque et al. (2010 , 2011 ), and phosphorus deposition is informed by Brahney et al. (2015) and Chien et al. (2016) . The model runs with fully transient CO2 and nutrient deposition, coupled C-N cycling and prescribed P. Before running the main simulations, QUINCY conducts a spin-up phase of 500 years to stabilize vegetation and soil pools repeating data drawn from 1901–1930, with an accelerator applied to slow soil pools. Following spin-up, simulations for the sites commence in 1901 and continue through 2019. 2.3 Phenological scenarios We conducted simulations under unaltered (S0) and altered meteorological conditions, modifying one climatic forcing variable for each scenario in order to evaluate their individual impact on vegetation processes, and the capacity of each detection method to capture it. Perturbations were imposed to climate data for a year that was common across all sites and did not include extremes (2005). As phenological studies most frequently focus on warm springs ( Hassan et al., 2023 ), we did not include this scenario in our analysis, instead choosing other seasonal perturbation scenarios likely to be caused by climate change. In particular, we tested: (S1) low spring temperatures, with a 2°C temperature reduction from Day of Year (DOY) 32 to 120 (February to April), (S2) spring drought, with a 75% reduction in precipitation from DOY 32 to 151 (February to May), (S3) legacy of summer drought, with a 75% reduction in precipitation from DOY 121 to 212 (May to July) of the previous year, (S4) high autumn temperatures, with a 2°C temperature increase from DOY 213 to 304 (August to October), (S5) slow warming spring, with a 15-day delay in temperature increase from DOY 32 to DOY 120 (February to April). 2.4 Calculation of Start and End of Season From the time series of simulated daily values of GPP and LAI from each scenario, we estimated phenological transition dates using the R package phenofit ( Kong et al., 2022 ). To produce a smoothed curve from each time series, we first rough-fitted the data with a weighted Harmonic ANalysis of Time Series (wHANTs) function ( Verhoef, 1996 ; Yang et al., 2015 ), then performed weight updating using the TIMESAT function ( Jönsson & Eklundh, 2004 ), and last fine fitted the curve using the Elmore logistic approach ( Elmore et al., 2012 ). We selected this combination of functions as the most appropriate to the vegetation type in this study, following recommendations from Kong et al. 2022 ). Phenological transition dates were then estimated on the resulting smoothed GPP and LAI values for the entire year using: Threshold Method (TRS) : SOS/EOS are identified as the first days when the normalized vegetation time series crosses set thresholds (10%, 20%, or 50%) of its seasonal amplitude ( Fang et al., 2023 ). Derivative Method (DER) : SOS/EOS are identified as the days when the first derivative of the vegetation time series (rate of change) reaches its maximum or minimum values ( Fisher et al., 2006 ). Gu Method (GU) : SOS/EOS are identified as the days where the lower boundaries of the vegetation time series intersect with recovery and senescence lines. These lines are defined by the slopes of two tangent lines that pass through the time series’ maximum and minimum points ( Gu et al., 2009 ). Inflection Method (INFL): SOS/EOS are identified as the days when the rate of change in the angle of the tangent vector for the vegetation time series reaches a local maximum or minimum ( Klosterman et al., 2014 ; Zhang et al., 2003 ). More details for these methods are provided in Kong et al. (2020 , 2022 ). We then compared the estimated SOS and EOS with the temperature-triggered growing season provided by QUINCY. A workflow overview is presented in Fig.1 . Download figure Open in new tab Figure 1. Workflow used for estimating the Start (SOS) and End (EOS) of the growing Season (in yellow) from simulations at four temperate forest sites (FR-Fon, US-Ha1, DE-Hai, US-MMs) across 6 climate scenarios, reported in the figure. QUINCY SOS/ EOS, Gross Primary Productivity (GPP) and Leaf Area Index (LAI) are provided as outputs of each simulation. The latter two are used to extract SOS/ EOS using the workflow from Kong et al. (2022) with Threshold 10% (TRS 10), Threshold 20% (TRS 20), Threshold 50% (TRS 50), Derivative method (DER), Gu method (GU) and Inflection method (INF). 3. Results Under unaltered meteorological conditions, sites exhibit distinct temperature-dependent growing seasons (QUINCY SOS/ EOS in S0, Fig.2 - 3 ). Notably, the site FR-Fon displays the longest growing season, as well as the earliest start (234 days, SOS DOY 87, EOS DOY 321), followed by US-MMs (214 days, SOS DOY 108, EOS DOY 322), DE-Hai (194 days, SOS DOY 122, EOS 316), and US-Ha1 (162 days, SOS DOY 134, EOS DOY 296) ( Fig. 2 - 3 , Dataset S1). These differences reflect the climatological contexts of each site, where FR-Fon and US-MMs experience the highest temperatures and US-Ha1 on the other hand is characterized by the coldest conditions ( Table 2 , Fig. S1). Similar patterns are observed in the growing seasons calculated from simulated GPP and LAI values across all six detection methods, with method-specific differences appearing consistently across sites and scenarios ( Fig. 2 - 3 , Table S1). Specifically, the inflection method provides the closest dates to the QUINCY SOS across sites and scenarios for both GPP (-2 to 3 days discrepancy) and LAI (up to 4 days discrepancy), although we observe missing EOS dates in US-MMs for S1, S2, S4 and S5. The second closest dates to the QUINCY SOS are provided by the 10% threshold method, with a maximum discrepancy of 4 days for GPP and 7 days for LAI. Notably, the LAI-derived SOS consistently occurs later than GPP SOS, regardless of method or scenario. The closest dates to the QUINCY EOS are instead observed at the 20% threshold for GPP (-9 to 10 days discrepancy), and both the 50% threshold and derivative method for LAI (both showing about 10 days of discrepancy). In general, the 50% threshold and derivative methods delineate the narrowest growing seasons with the latest SOS and earliest EOS and very similar dates, regardless of scenario (Table S1). Download figure Open in new tab Figure 2. Start (SOS) and End of growing Season (EOS) dates calculated through QUINCY, Threshold 10% (TRS 10), Threshold 20%(TRS 20), Threshold 50% (TRS 50), Derivative method (DER), Gu method (GU) and Inflection method (INF) on simulated GPP at unaltered conditions (S0), cold spring (S1), dry spring (S2), dry previous summer (S3), warm autumn(S4), slow warming spring (S5) at DE-Hai, FR-Fon, US-Ha1, US-MMs: (a) SOS dates across sites and scenarios, (b) GPP increase across scenarios at SOS and dates provided by methods, (c) GPP decrease across scenarios at EOS and dates provided by methods, (d) EOS dates across sites and scenarios. Download figure Open in new tab Figure 3. Start (SOS) and End of growing Season (EOS) dates calculated through QUINCY, Threshold 10% (TRS 10), Threshold 20%(TRS 20), Threshold 50% (TRS 50), Derivative method (DER), Gu method (GU) and Inflection method (INF) on simulated LAI at unaltered conditions (S0), cold spring (S1), dry spring (S2), dry previous summer (S3), warm autumn(S4), slow warming spring (S5) at DE-Hai, FR-Fon, US-Ha1, US-MMs: (a) SOS dates across sites and scenarios, (b) GPP increase across scenarios at SOS and dates provided by methods, (c) GPP decrease across scenarios at EOS and dates provided by methods, (d) EOS dates across sites and scenarios. The perturbation scenarios further highlight differences in method sensitivity to site climatic conditions ( Fig.4 ). In the case of low spring temperatures (S1), QUINCY SOS is delayed by 6 to 16 days compared to unaltered conditions, where US-Ha1 is the least and DE-Hai the most affected, with SOS values considerably more delayed in the latter site compared to the others. Similarly, S1 causes a delay in the GPP based SOS regardless of detection method, although to a lesser degree compared to the QUINCY SOS. In particular, the GPP-SOS occurs 1 to 3 days earlier than QUINCY SOS in all sites except for FR-Fon, where the GPP-SOS occurs instead 1 to 2 days later than the QUINCY SOS, depending on method. The LAI-based SOS for S1 are on the other side closer to the QUINCY SOS across detection methods, particularly in FR-Fon and US-Ha1. S1 does not affect QUINCY and LAI EOS, but instead delays GPP EOS of 1 to 2 days, depending on site and detection method. Download figure Open in new tab Figure 4. Day difference between Start of Season (SOS) and End of Season (EOS) dates at unaltered conditions (dotted line) and respective dates at cold spring (S1), dry spring (S2), dry previous summer (S3), warm autumn(S4), and slow warming spring (S5) at DE-Hai, FR-Fon, US-Ha1, US-MMs. Dates calculated through QUINCY, Threshold 10% (TRS 10), Threshold 20% (TRS 20), Threshold 50% (TRS 50), Derivative method (DER), Gu method (GU) and Inflection method (INF) on GPP and LAI simulated values. Dry spring conditions (S2) generally do not have any effects on the QUINCY, GPP, and LAI SOS, although with notable exceptions. For instance an up to 2-day early GPP SOS is evident in FR-Fon across all methods. In the opposite direction, LAI SOS has a 1-day delay in US-Ha1 at the 50% threshold and a 1-day delay in DE-Hai at the derivative method. The GPP EOS also shows a delay of up to 3 days across sites, varying by method. Summer drought legacy (S3) does not affect the subsequent growing season in QUINCY but impacts both GPP and LAI SOS, showing delays of up to 5 days, depending on site and method. Similar to dry spring conditions, only the GPP EOS shows a delay across sites, with FR-Fon being the most affected site regardless of the method. High autumn temperatures (S4) impact QUINCY EOS only in US-Ha1 and DE-Hai, causing a 4 and 2-days delay respectively. These same sites show similarly delayed GPP and LAI EOS, but in addition to that all methods also detect an up to 2 days early GPP EOS in FR-Fon. Interestingly, FR-Fon also shows a 1-day delay in LAI EOS at the 50% threshold. No effects on SOS were expected as temperature changes have been implemented later in the season, however we still observe 1-day differences in GPP SOS in FR-Fon at the derivative, Gu and Inflection method, and LAI SOS in US-Ha1 at the derivative method. Under slow warming spring temperatures (S5), the SOS in QUINCY is delayed by 10 to 14 days, again depending on site. The least affected is FR-Fon, while the most pronounced delay occurs in US-Ha1. The GPP SOS is also delayed, although it occurs 1 to 5 days before the corresponding QUINCY SOS, with the largest advance noted in DE-Hai. The LAI SOS is instead generally closer to QUINCY SOS dates, similar to what is observed in S1. Similarly, the EOS for both QUINCY and LAI largely align with minimal variation. Only FR-Fon exhibits a 1-day earlier EOS detected by the inflection method, and US-Ha1 a 1 day late EOS detected by the 10% threshold. In contrast, the GPP EOS shows a consistent delay of 1 to 3 days across all sites and methods. 4. Discussion 4.1 Phenological detection method agreement All tested methods provide GPP and LAI-derived SOS/EOS dates that accurately reflect site-specific environmental conditions and align with QUINCY’s temperature-dependent growing season. The warmest sites (FR-Fon, US-MMs) show the earliest and longest growing seasons, while the coldest and wettest site (US-Ha1) exhibits the latest onset and shortest duration ( Fig. 2 - 3 ). This aligns with expectations as higher temperatures accelerate and extend vegetative activity, increasing photosynthesis, LAI, and GPP, as confirmed by multiple detection methods. This reflects the expected behavior of temperate forest phenology, where temperature increase drives the transition from dormancy to active growth, and temperature decrease co-regulates leaf senescence ( Calinger & Curtis, 2023 ; Campioli et al., 2024 ; Silvestro et al., 2025 ). Growing season responses are consistent across methods and driven by local conditions ( Fig. 3 ). Under cooler or slowly warming spring scenarios (S1, S5), both QUINCY and detection methods show delayed SOS, consistent with literature (Q. Wang et al., 2024 ; Xiong et al., 2023 ). EOS is also delayed, but only when derived from GPP, which is more temperature-sensitive due to its direct link to photosynthesis and stress limitations. LAI, influenced by leaf biomass growth, shows smoother seasonal changes. In warm autumn conditions (S4), GPP-derived EOS advances in the driest site (FR-Fon), but is delayed in colder sites (US-Ha1, DE-Hai), reflecting context-dependent relationships (Q. Wang et al., 2024 ). Non-traditional scenarios (e.g., drought) primarily affect GPP and LAI-derived seasons. Spring drought (S2) advances GPP SOS in FR-Fon, while summer drought legacy (S3) delays both GPP and LAI SOS but only prolongs GPP seasons, likely due to reduced growth and storage in the prior season. These differential effects are expected and context-dependent ( Bórnez et al., 2021 ; Duan et al., 2018 ). 4.2 Phenological detection method specificities Between-method differences in phenological dates are maintained across scenarios. The inflection method provides the closest SOS dates to QUINCY for both GPP and LAI, as it detects local maxima/minima in the curvature’s rate of change ( Zhang et al., 2003 ), aligning with ground-based, temperature-dependent leaf-out dates ( Klosterman et al., 2014 ). However, its reliance on four inflection points can lead to missing data when conditions are not met ( Kong et al., 2022 ), as seen with missing GPP EOS dates in US-MMs (Table S1, Dataset S1), likely due to high temperatures and precipitation ( Figure 2 ). The second closest method to QUINCY SOS is the 10% threshold. Also in this case, low thresholds have been shown to provide close dates to temperature-dependent early plant activity on the ground ( Panwar et al., 2023 ). The 10% threshold is the second closest to QUINCY SOS, reflecting early plant activity ( Panwar et al., 2023 ). For EOS, most methods yield dates later than QUINCY, especially for LAI ( Fig. 3 , Table S1), attributed to model parametrization and the timing of LAI decrease as leaf shedding only starts in the model once the EOS trigger is reached. The closest EOS dates to QUINCY are from the 20% threshold for GPP ( Fig. 2 ) and 50% threshold/derivative method for LAI ( Fig. 3 ), supporting the use of lower thresholds for SOS and higher for EOS in the context of this study ( Panwar et al., 2023 ). The 50% threshold and derivative method consistently show the latest SOS and earliest EOS, defining the narrowest growing season and demarcating the peak of season (POS). The reliance of POS dynamics on a different set of factors other than SOS/ EOS such as early-season vegetation activity and developmental constraints, and the effect of POS shifts on maximum resource availability support the need to study it as separate from SOS/ EOS dynamics, a point already noted in the literature ( Li et al., 2025 ; Liu et al., 2025 ). These methods indeed respond distinctly to simulated conditions, particularly under summer drought legacies (S3), with pronounced delays in SOS at the driest site, FR-Fon. It is worth highlighting that while phenological dates are generally consistent across methods, some exceptions occur. For instance, summer drought (S3) in DE-Hai delays SOS using the 50% threshold and derivative methods, yet advances it by one day with the Gu method. Another case can be made for the warm autumn scenario (S4) showing one-day shifts in SOS under the derivative, Gu, and inflection methods, despite changes in conditions occurring later in the season. These discrepancies, rarely exceeding one day, likely arise from SOS/EOS calculations being based on annual fitted values, reflecting year-round rather than seasonal variations. Detection methods depend on the shape of the vegetation time series, so any factor altering this shape can impact phenological assessments. 4.3 Beyond temperature threshold effects in models This study has shown that, for the scenarios investigated, non-temperature driven changes in climatic conditions have effects on the growing season, including between-site dynamics, which would not be picked up by temperature threshold methods and models but can still be captured by standard methods for detecting SOS and EOS. In particular, we observe nuanced SOS-POS-EOS responses to environmental changes detected on simulated GPP and LAI that are missed in the QUINCY growing season ( Fig.3 ), where internal SOS/ EOS are triggered solely by temperature, a common approach in phenology models (Dávid et al., 2021; Fu et al., 2014 ; Mo et al., 2023a). Phenological models that exclusively rely on temperatures oversee factors that influence plant responses to environmental changes, such as the joint effects of chilling, forcing, photoperiod, precipitation ( Liang et al., 2024 ; Mo, et al., 2023b; Mo, Zhang, et al., 2023 ), and legacy effects ( Keenan & Richardson, 2015 ; Liu et al., 2024 ), and ultimately affect the accuracy of the simulated growing season. Further, this effect might be missed in regional or global studies that correlate phenological dates derived from vegetation time series with increases in temperatures, without taking into account other drivers ( Dávid et al., 2021b ; J. Wang et al., 2021 ). A more realistic representation of the growing season beyond GDD type thresholds in LSMs (e.g. Caldararu et al., 2012 , 2014 , Seitz et al., in prep) would be necessary for accounting said dynamics and better simulate plant phenology (Mo, et al., 2023b). 4.4 Models as a tool for theoretical exploration Here we use QUINCY simulations as baseline ‘truth’ - we know when the growing season starts and ends and we can alter environmental conditions to explore the agreement between phenological detection methods. This would have been impossible with observations, as differences between growing seasons would be difficult to attribute to methods or the effect of local environmental conditions univocally. Process based models such as QUINCY are unique tools for creating ecologically realistic data that can easily be manipulated, essentially creating a large number of ecosystem experiments in silico. While the generation of synthetic data from models for testing has already been applied in ecology ( DiRenzo et al., 2023 ; Fernández et al., 2024 ; Figueira & Vaz, 2022 ; Rahman et al., 2020 ; Zhan et al., 2022 ) this is, to our knowledge, the first time a LSM is used for this purpose. Our study paves the way for a utilisation of LSMs beyond their current use as predictive tools and extend their potential for theoretical exploration. It is of course important to keep in mind that by using model simulations any assumptions already existing in the model are incorporated in the study. For example, the GDD and chilling requirement parameterisation affects the magnitude of changes in any of our scenarios and implicitly the capacity of each method to detect such changes. Representations of soil hydrology and plant response to water stress will in turn influence the response to the water based scenarios. Future studies could include multiple LSMs and a more comprehensive uncertainty analysis. Conclusions In this study we compare commonly used phenological detection methodologies on four temperate forest sites, where we use the QUINCY LSM to simulate GPP and LAI under five different climate scenarios. Despite method specific differences that remain largely consistent across scenarios, the growing season provided by model simulations and methods largely agree, responding to changes according to site-specific environmental context. Methods sensitive to the earliest alterations provide the closest match to a temperature-only dependent SOS, while higher thresholds are closer to the EOS provided by the model. This likely reflects the reliance of early spring and autumn phenology events on temperatures. Highest thresholds and methods based on the rate of change of vegetation parameters delimit peak of season, which can be affected differentially by environmental forcings. We also show how some methods, despite broadly agreeing in their response to simulated changes, are also able to pick up on effects that were overlooked by the GDD-threshold type growing season, such as the one provided by QUINCY. This warrants discussion for rethinking a threshold based growing season within process based models, in favour of more realistic representations of phenology. Despite these limitations, QUINCY provided good ground for the generation of synthetic data to be used for testing phenological detection methodologies. This is, to our knowledge, the first time an LSM is used for the purpose of phenological benchmarking, paving the way for extending the use of LSMs beyond their current application as predictive tools. Conflict of Interest The authors declare no competing interest Author Contribution SC conceptualised the study and supervised the work, MY conducted the analyses, JS tested the workflow for extracting phenological dates, WJM, LD and GGN provided feedbacks on the data processing. All authors contributed to the revision of the manuscript. Data Availability The data that support the findings of this study is available in the Supporting Information. The R code used to generate the data can be found at [link to GitHub repo after review]. The input PLUMBER2 NetCDF data are publicly available at https://dx.doi.org/10.25914/5fdb0902607e1 . The QUINCY model’s source-code is available at https://git.bgc-jena.mpg.de/quincy/quincy-model-releases . The access to this git repository is restricted to registered users and can be made available upon request under the BSD-3-Clause license. More information at https://doi.org/10.17871/quincy-model-2019 . Statement on Inclusion This study is based entirely on simulated data. All empirical data used to inform the simulations were obtained from publicly available repositories, cited in the text. Consequently, no local data were collected. The simulated sites represent ecosystems in the Northern Hemisphere, contributing to the existing body of ecological research focused on this region. While this focus reflects the availability of data and established modelling frameworks, we acknowledge that ecological patterns and processes may differ in other underrepresented regions. Acknowledgements We thank Dr Camille Abadie for her thoughtful feedback and helpful suggestions, which contributed to the improvement of this manuscript. MY was funded through Trinity Research Doctorate Awards. JS was funded through iCRAG SFI grant. LD was funded through the Research Ireland Frontiers for the Future project trait-Tweaks 22/FFP-P/11474. GGN was funded through the Co-Centre for Climate + Biodiversity + Water that has the financial support of Research Ireland, Northern Ireland’s Department of Agriculture, Environment and Rural Affairs (DAERA), UK Research and Innovation (UKRI) via the International Science Partnerships Fund (ISPF) under Grant number [22/CC/11103] at the Co-Centre for Climate + Biodiversity + Water. Funder Information Declared Trinity Research Doctorate Awards iCRAG Research Ireland Frontiers for the Future Co-Centre for Climate + Biodiversity + Water Footnotes myajima{at}tcd.ie , seitzj{at}tcd.ie , matthaew{at}tcd.ie , girishng{at}tcd.ie , dalyl14{at}tcd.ie , caldaras{at}tcd.ie References ↵ Abernethy , K. , Bush , E. R. , Forget , P.-M. , Mendoza , I. , & Morellato , L. P. C. ( 2018 ). Current issues in tropical phenology: A synthesis . 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